AI in Healthcare: How LLMs are personalizing medicine through genomic analysis

LLMs, based on transformer architectures, possess the unique ability to process vast volumes of textual and numerical data simultaneously, a feature that enables them to decode intricate genomic patterns. These models have shown exceptional skill in analyzing DNA and RNA sequences, much like processing natural language. Through context-aware predictions, LLMs can identify variants, assess their biological impact, and suggest possible disease linkages.

Artificial intelligence (AI) continues to redefine the landscape of healthcare, with large language models (LLMs) emerging as a critical force in the drive toward personalized medicine. A recent review titled “Large Language Models in Genomics—A Perspective on Personalized Medicine,” published in Bioengineering in April 2025, provides a comprehensive examination of how LLMs are revolutionizing genomics, precision diagnostics, and tailored treatment plans.

The study explores the transformative potential of LLMs, their application in decoding genomic complexity, and their role in developing individualized medical interventions. It also addresses current limitations and outlines a roadmap for future research in integrating LLMs into clinical practice.

How Are LLMs Reshaping Genomic Analysis?

LLMs, based on transformer architectures, possess the unique ability to process vast volumes of textual and numerical data simultaneously, a feature that enables them to decode intricate genomic patterns. These models have shown exceptional skill in analyzing DNA and RNA sequences, much like processing natural language. Through context-aware predictions, LLMs can identify variants, assess their biological impact, and suggest possible disease linkages.

The report highlights various models tailored for biological and medical applications, such as DNABERT for DNA sequence analysis, ChemBERTa for drug discovery, and ProteinBERT for protein interaction studies. These tools allow researchers to move beyond static databases, enabling dynamic exploration of gene expressions and molecular structures in real-time.

Beyond basic sequence analysis, LLMs are helping construct advanced gene-regulatory networks by integrating diverse omics datasets — from transcriptomics to proteomics. Tools like GROVER and DeepMAPs showcase how LLMs can generate biologically meaningful insights through complex pattern recognition across heterogeneous datasets.

LLMs are also enhancing drug discovery pipelines. By understanding the structural behavior of proteins (e.g., through AlphaFold or ProteinGPT), these models accelerate the identification of therapeutic targets and improve the customization of pharmaceutical compounds based on an individual’s genetic blueprint.

What Are the Key Applications in Precision Medicine?

Precision medicine seeks to tailor medical care to the individual, considering genetic, environmental, and lifestyle differences. LLMs play a pivotal role by integrating genomic data into clinical decision-making processes. Through their contextual learning capabilities, these models support the identification of disease markers, aid diagnosis, and even recommend customized treatment regimens.

The integration of LLMs with high-throughput data sources such as the Human Genome Project, ENCODE, and The Cancer Genome Atlas has enabled unparalleled insights into disease progression and drug response variability. The models also facilitate the interpretation of data from next-generation sequencing (NGS) and single-cell RNA sequencing (scRNA-seq), helping to capture previously inaccessible insights into cellular diversity and disease pathways.

For example, CancerGPT leverages few-shot learning techniques to predict synergistic drug combinations for rare cancer types, addressing a major bottleneck in oncological therapy design. Simultaneously, the development of multimodal LLMs like ProteinGPT supports comprehensive analysis by combining genomic, proteomic, and structural data — a crucial advantage in multi-omics-based treatment strategies.

In addition to diagnosis and therapy optimization, LLMs serve as intelligent assistants in clinical decision support, summarizing medical literature, generating reports, and identifying knowledge gaps in real time. Their utility spans from hospital readmission prediction using models like ClinicalBERT to treatment recommendations via MedBERT and BioBERT.

What Are the Challenges and the Path Ahead?

While the promise of LLMs in healthcare is substantial, significant challenges remain. Data sparsity, especially in single-cell genomics, continues to hamper model training and accuracy. Techniques such as the Mixture of Experts (MoE) and hybrid model architectures are being explored to address this issue by routing data through specialized sub-networks.

Interpretability also remains a major hurdle. Since clinical decision-making requires transparency, the opaque nature of deep learning models limits their acceptability among practitioners. However, advances in explainable AI (XAI), including tools like SHAP and LIME, are helping demystify model outputs, enhancing trust and usability in healthcare settings.

Computational resource demands pose another constraint. LLMs require immense GPU power and storage, often inaccessible to smaller research institutions. Techniques like knowledge distillation, pruning, and quantization are being used to create leaner, more efficient models suitable for clinical environments.

Model generalization across diverse populations is yet another critical concern. The inherent non-linearity and three-dimensional structure of genomic data challenge LLMs traditionally trained on text. Efforts to improve model accuracy include incorporating 3D genomic architecture and epigenetic data into training workflows, as well as adopting frameworks like DeepMAPS and scMoFormer for multi-modal predictions.

The study underscores the pressing need for robust data governance. Given the sensitivity of genomic information, privacy-preserving strategies such as federated learning, anonymization, and differential privacy are essential. These tools are not only vital for ethical compliance but also for enabling collaborative, cross-border research without compromising patient security.